Advanced model predictive control strategies for evaporation processes in the pharmaceutical industries

被引:2
|
作者
Nascu, Ioana [1 ]
Diangelakis, Nikolaos A. [2 ]
Munoz, Salvador Garcia [3 ]
Pistikopoulos, Efstratios N. [4 ,5 ]
机构
[1] Tech Univ Cluj Napoca, Fac Automat & Comp Sci, Dept Automat, Cluj Napoca 400114, Romania
[2] Tech Univ Crete, Sch Chem & Environm Engn, Khania 73100, Greece
[3] Eli Lilly & Co, LillyResearch Labs, Synthet Mol Design & Dev, Indianapolis, IN 46074 USA
[4] Texas A&M Univ, Texas A&M Energy Inst, College Stn, TX USA
[5] Texas A&M Univ, Artie McFerrin Dept Chem Engn, College Stn, TX USA
关键词
Evaporation process; Pharmaceuticals; Process control; PID; MPC; Multiparameric; explicit model based; predictive control; OPTIMIZATION; DESIGN; QUALITY; PERSPECTIVES;
D O I
10.1016/j.compchemeng.2023.108212
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
In this paper we present a framework to design control systems for an evaporation process in the pharmaceutical industry with the aim to deliver guaranteed operability for different molecules and under different thermody-namic scenarios. Based on a mathematical model developed within the gPROMS platform calibrated and vali-dated with real data from experiments, three control methods are implemented and compared, (i) Proportional Integrative Derivative control (PID), (ii) Model Predictive Control (MPC) and (iii) explicit/multi-parametric Model Predictive Control (mp-MPC). The performance and limits of the derived control schemes are then established and tested for reference tracking as well as disturbances rejection.
引用
收藏
页数:16
相关论文
共 50 条
  • [41] Comparative Analysis of Distributed Model Predictive Control Strategies
    Maxim, Anca
    Caruntu, Constantin F.
    Lazar, Corneliu
    De Keyser, Robin
    Ionescu, Clara M.
    2019 23RD INTERNATIONAL CONFERENCE ON SYSTEM THEORY, CONTROL AND COMPUTING (ICSTCC), 2019, : 468 - 473
  • [42] Optimization of Temporal Processes: A Model Predictive Control Approach
    Song, Zhe
    Kusiak, Andrew
    IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2009, 13 (01) : 169 - 179
  • [43] Model predictive control algorithm for nonlinear chemical processes
    Tiagounov, AA
    Weiland, S
    2003 INTERNATIONAL CONFERENCE PHYSICS AND CONTROL, VOLS 1-4, PROCEEDINGS: VOL 1: PHYSICS AND CONTROL: GENERAL PROBLEMS AND APPLICATIONS; VOL 2: CONTROL OF OSCILLATIONS AND CHAOS; VOL 3: CONTROL OF MICROWORLD PROCESSES. NANO- AND FEMTOTECHNOLOGIES; VOL 4: NONLINEAR DYNAMICS AND CONTROL, 2003, : 334 - 339
  • [44] Partitioning for distributed model predictive control of nonlinear processes
    Rocha, Rosiane R.
    Oliveira-Lopes, Luis Claudio
    Christofides, Panagiotis D.
    CHEMICAL ENGINEERING RESEARCH & DESIGN, 2018, 139 : 116 - 135
  • [45] Model predictive functional control for processes with unstable poles
    Electrical Engineering, University of Ljubljana, Slovenia 251000, China
    Asian J. Control, 2008, 4 (507-513):
  • [46] ROBUST MODEL PREDICTIVE CONTROL OF PROCESSES WITH HARD CONSTRAINTS
    ZAFIRIOU, E
    COMPUTERS & CHEMICAL ENGINEERING, 1990, 14 (4-5) : 359 - 371
  • [48] Model predictive control of interconnected linear and nonlinear processes
    Zhu, GY
    Henson, MA
    INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, 2002, 41 (04) : 801 - 816
  • [49] Model Predictive Control for Blending Processes in Cement Plants
    Zhang, Zhanhao
    Nielsen, Marcus Krogh
    Muralidharan, Guruprasath
    Horsholt, Steen
    Jorgensen, John Bagterp
    IFAC PAPERSONLINE, 2022, 55 (07): : 483 - 488
  • [50] Model predictive control for nonlinear processes with varying dynamics
    Sayyar-Rodsari, B
    Axelrud, C
    Liano, K
    HYDROCARBON PROCESSING, 2004, 83 (10): : 63 - +